AIGMob: Conditional Generative AI for Fine-grained Urban Mobility Simulation

Research Project

Project/Area Number	24K02996
Research Category	Grant-in-Aid for Scientific Research (B)
Allocation Type	Multi-year Fund
Section	一般
Review Section	Basic Section 61030:Intelligent informatics-related Basic Section 60030:Statistical science-related Sections That Are Subject to Joint Review: Basic Section60030:Statistical science-related , Basic Section61030:Intelligent informatics-related
Research Institution	The University of Tokyo
Principal Investigator	姜仁河東京大学, 空間情報科学研究センター, 講師 (20865266)
Project Period (FY)	2024-04-01 – 2027-03-31
Project Status	Granted (Fiscal Year 2024)
Budget Amount *help	¥18,590,000 (Direct Cost: ¥14,300,000、Indirect Cost: ¥4,290,000) Fiscal Year 2026: ¥5,590,000 (Direct Cost: ¥4,300,000、Indirect Cost: ¥1,290,000) Fiscal Year 2025: ¥6,500,000 (Direct Cost: ¥5,000,000、Indirect Cost: ¥1,500,000) Fiscal Year 2024: ¥6,500,000 (Direct Cost: ¥5,000,000、Indirect Cost: ¥1,500,000)
Keywords	Generative AI / Human Mobility / Mobility Simulation / Spatiotemporal Data
Outline of Research at the Start	The goal of this research is to develop conditional generative AI framework for fine-grained urban mobility simulation. This framework will stand as an entirely data-powered pipeline, adeptly producing diverse trajectories based on prevailing traffic demands and specific traffic state conditions.